The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg–Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

The main objective of this study is to identify and develop a comprehensive model which estimates and evaluates the overall relations among the factors that lead to weight gain in children by using structural equation modeling. The proposed models in this study explore the connection among the socioeconomic status of the family, parental feeding practice, and physical activity. Six structural models were tested to identify the direct and indirect relationship between the socioeconomic status and parental feeding practice general level of physical activity, and weight status of children. Finally, a comprehensive model was devised to show how these factors relate to each other as well as to the body mass index (BMI) of the children simultaneously. Concerning the methodology of the current study, confirmatory factor analysis (CFA) was applied to reveal the hidden (secondary) effect of socioeconomic factors on feeding practice and ultimately on the weight status of the children and also to determine the degree of model fit. The comprehensive structural model tested in this study suggested that there are significant direct and indirect relationships among variables of interest. Moreover, the results suggest that parental feeding practice and physical activity are mediators in the structural model.

Introduction: Eating behaviour pattern is among the key behavioural factors that contribute to eating disorders. Hence, to evaluate the psychometric characteristics of the Eating Behaviour Pattern Questionnaire (EBPQ) that is used in epidemiological studies to measure the relationship between health outcomes and eating behaviour patterns, this study aimed to validate the adopted version of the EBPQ and to check the validity and reliability of this tool in University of Malaya, Malaysia. Methods: Exploratory factor analysis (EFA) was used to determine the most appropriate factor structure of EBPQ. Moreover, structural equation modelling (SEM) and confirmatory factor analysis (CFA) were applied to examine the convergent and discriminant validity of EBPQ. As for the participants of the study, multi-stage random sampling was used and 200 students (109 females and 91 males) from University of Malaya were chosen. Results: The EFA yielded nine components of EBPQ including emotional eating, eating outside, cultural habit, low-fat eating, meal skipping, snacking, healthy eating, planning for food and sweets, which explained 67.7% of the total variance. Furthermore, the Cronbach's α was about 0.8 for all components, which exhibited a high internal consistency among the obtained components. The results showed that the questionnaire had sufficient convergent and discriminant validity. Conclusion: The EBPQ was proven to be a reliable tool to measure the eating behaviour patterns in Malaysian university students. The presence of adequate validity and reliability supports this instrument's psychometric properties for future studies.

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